DNP 830 Benchmark What Are the Data Saying?

Grand Canyon University DNP 830 Benchmark What Are the Data Saying?  – Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University DNP 830 Benchmark What Are the Data Saying? assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

How to Research and Prepare for DNP 830 Benchmark What Are the Data Saying?      

Whether one passes or fails an academic assignment such as the Grand Canyon University NUR 550 Benchmark – Evidence-Based Practice Project: Literature Review depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

How to Write the Introduction for DNP 830 Benchmark What Are the Data Saying?      

The introduction for the Grand Canyon University DNP 830 Benchmark What Are the Data Saying?        is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

How to Write the Body for DNP 830 Benchmark What Are the Data Saying?      

After the introduction, move into the main part of the DNP 830 Benchmark What Are the Data Saying?       assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

How to Write the Conclusion for DNP 830 Benchmark What Are the Data Saying?      

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

How to Format the References List for DNP 830 Benchmark What Are the Data Saying?      

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

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Sample Answer for DNP 830 Benchmark What Are the Data Saying?

Data analysis and interpretation form a critical part of research since it leads to the fulfillment of the project objectives. Through data interpretation, the researchers are capable of fully understanding the results in relation to the research questions and objectives since the raw data may not provide useful insights and meaning in connection to the project. The implication is that the data should carefully be interpreted following the laid down principles to get the full meaning. Upon collecting the raw data, the data is then taken through statistical analysis and then represented in graphs, charts, and percentages as a way of data visualization (Kim et al.,2020). As such, the purpose of this assignment is to carry run appropriate statistics in SPSS, provide the results for the analyzed data, and compose an analysis explaining the procedure used in the analysis of the non-parametric and parametric variables.

The Statistical Tests Used Independent Sample T-test

This is a parametric test usually applied to explore if two groups or populations have a similar mean based on a particular variable. The independent T-test is used when the data to be analyzed has a continuous dependent variable not related within the groups, a categorical independent variable of at least two groups, while the data should have a normal distribution and must be a random sample (Gerald, 2018). In addition, the data to be analyzed should possess an equal variance across the group with no outliers. Besides, every group must also have at least six study subjects, with each group having an equal number of study participants. As such, in the cases of the data provided, this test was chosen to help in exploring and determining the means between the two groups provided.

Paired Sample T-test

This is a statistical test usually used in comparing means from the same data sample to find out if the compared means are significantly different. Therefore, paired sample T-test is usually used in research designs such as control experiments, pre-test and post-test, and experimental designs. It is particularly applied when a researcher needs to make comparisons between two points, measurements, conditions, or matched pair (Afifah et al.,2022). It is worth noting that the tests are not applicable in cases where there are unpaired samples with no normal distribution around the mean and have more than two units. This test has been chosen since there is a need to compare the weight at baseline and the intervention weight with the main focus of determining if there is a significant difference. As such, through the test, it will be possible to determine if there has been a change in weight.    

McNemar

This is another test that has been applied to the provided data sample. The McNemar test is used as a way of checking the marginal homogeneity of two different dichotomous variables.  As such, it is used for two groups having similar participants and when the data is paired. The data to be analyzed should have an independent variable with two related groups, and the groups to be considered should be mutually exclusive and with a random sample (Pembury Smith & Ruxton, 2020). Therefore, the McNemar test was used in this case for comparing the compliance of the baseline to that of the intervention for the research subjects’ data.           

Chi-square

This statistical test is usually applied in determining how two variables are associated; hence it is also referred to as the chi-square test of association. While it is key to identifying associations between variables, it cannot be used to draw inferences. For the Chi-Square test to be used, the data under consideration should have two categorical variables at least two categories in every variable (Connelly, 2019). The subjects should also be of a large sample and unrelated. As such, this test was also chosen in this case since there was a need to explore the intervention readmission and baseline readmission associations.

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DNP 830 Topic 8 Reflective Journal 

DNP 830A Data Analysis

Wilcoxon Z is a statistical test used to compare the means of related samples. As such, it is applied in the analysis of repeated measures without or with intervention. Therefore, this test can be used in cases where there are matched subjects without an intervention and another with an intervention (Kim et al.,2020). For it to be used appropriately, the pair should be from a random sample and also independent of other pairs. This test was chosen to help in comparing the mean ranks of the intervention weight pairs and those of the baseline weight.

Mann Whitney U

Mann Whitney U test is a statistical test used when comparing the difference between independent samples that do not possess normal distribution. For the test to be used, all the variables should be in ordinal or continuous scales. In most cases, this test is used in cases when one of the considered parameters does not allow the use of independent sample t-tests (Kim et al.,2020). As such, the statistical test was used as there was a need to evaluate if there was a difference in satisfaction between the intervention and baseline data.

Parametric and Non-Parametric Tests

Parametric and non-parametric tests are both used in data tests and analysis. The parametric tests are tests that make the assumption that sample data or population has a normal distribution around the mean. Examples of these tests include paired t-test, 2-sample test, 1-sample t-test, and one-way ANOVA. On the other hand, the non-parametric tests do not consider such an assumption; hence using such an assumption during the analysis may lead to inappropriate interpretations (Orcan, 2020). Some of the non-parametric tests include the 1-sample Wilcoxon test, Mann-Whitney Test, Signed-rank test, and Kruskal Wallis test. The non-parametric test is used in cases when meaningful interpretations can be drawn from the median with data samples not appearing normal and small sample.

Summary of Results

Paired Sample T-test

Analysis was performed on the sample data provided. While the baseline weight mean was found to be 217.5 lbs, with an SD of 53.40, that of the intervention was found to be 178.3 lb, with an SD of 44.88. The results from the SPSS output show that t=7.188, df=29 t(df)=2.05, with a 95% confidence interval. The p-value is 0.000, and p<0.005. Therefore, the difference between the means was statistically significant.

Independent Sample T-test

This test shows that the mean weight for the intervention group is 218.3 lb with an SD of 54.8, a p-value of 0.934, and t (28) = 0,084 at a 95% confidence interval. The assumption made is that the variance is equal. The output t=0.084 is lower than 1.074, which is the critical value. Hence the result is insignificant. As such, the sampling variability affects the baseline weight and the intervention weight mean.  

McNemar

  The McNemar test shows that the frequency of events is 30 with a chi-square value of 1.639 and a p-value of 0.007. The implication is that it is statistically significant since it is lower than 0.05. As such, there is a difference between the intervention and baseline compliance is significant.

Chi-square

The analysis using Chi-Square shows a chi-square value of 1.639 with 1 df and a p-value of 0.008.  This value is lower than 3.84, which is the critical value. As such, the difference between the intervention and baseline readmissions is significant.

Wilcoxon Z

The analysis using Wilcoxon Z shows a mean difference of 11.5, Z=-4.307, and p=.000. The implication is that the mean ranking between baseline weight and intervention weight is statistically significant.

Mann Whitney U

The analysis using Mann Whitney U shows 18.8 and 12.2 as the baseline and intervention group mean, respectively. The Mann-Whitney test is 63.0 with a p-value of 0.035, a value lower than 0.05. The implication is that the result is that there is a statistical difference. The mean level of satisfaction is also lower in the intervention group in comparison to the baseline.

Conclusion

The SPSS software was used in analyzing the data to give results that can be interpreted to enhance an understanding of the collected data. Various statistical tests were used to obtain the required interpretation. Specifically, they offer appropriate information regarding the efficacy of the used intervention. For example, there was a significant difference in weight when an intervention was used. The groups also displayed a variance in both satisfaction and readmission.

References

Afifah, S., Mudzakir, A., & Nandiyanto, A. B. D. (2022). How to calculate paired sample t-test using SPSS software: From step-by-step processing for users to the practical examples in the analysis of the effect of application anti-fire bamboo teaching materials on student learning outcomes. Indonesian Journal of Teaching in Science, 2(1), 81-92. https://doi.org/10.17509/ijotis.v2i1.45895

Connelly, L. (2019). Chi-square test. Medsurg Nursing, 28(2), 127–127. https://www.proquest.com/openview/04d2ff080887f9111b68eb7490a9630a/1?pq-origsite=gscholar&cbl=30764

Gerald, B. (2018). A brief review of independent, dependent and one sample t-test. International Journal of Applied Mathematics and Theoretical Physics, 4(2), 50-54. Doi: 10.11648/j.ijamtp.20180402.13

Kim, M., Mallory, C., & Valerio, T. (2020). Statistics for evidence-based practice in nursing. Jones & Bartlett Publishers.

Orcan, F. (2020). Parametric or non-parametric: Skewness to test normality for mean comparison. International Journal of Assessment Tools in Education, 7(2), 255–265. https://doi.org/10.21449/ijate.656077

Pembury Smith, M. Q., & Ruxton, G. D. (2020). Effective use of the McNemar test. Behavioral Ecology and Sociobiology, 74, 1–9. Doi: 10.1007/s00265-020-02916-y

DNP 830 Working With Descriptive Statistics Sample

Working with Descriptive Statistics  

Descriptive statistics are used in describing features of data sets through a generation of summaries from a given data sample. Descriptive statistics can also be used to provide an overall summary of a study sample without interpretation (Grove & Cipher, 2019). When descriptive statistics are used in describing a population, various measures can be used to give a clearer picture of the population. The measures include median, mode, and mean as measures of tendency, standard deviation, and percentages (Kaur et al., 2018). The population data can be used to offer a summary of particular quantities in the sample in terms of percentages or mean. Such mean and percentages can then be used to help in understanding the trends existing in data. Therefore, the purpose of this paper is to develop a basic understanding of statistics by using a software program to analyze data using specific tests such as mean, median, mode, range, and standard deviation for a provided HCUP report. 

Summary of the Results 

The data were analyzed using SPSS software. SPSS has been widely used over the years for data analysis (Pallant, 2022). From the analysis, it was noted that there are both missing and valid variables. The analysis mainly focused on the number of discharges in the year 2018. In the analysis output window, the number of discharged individuals has been shown in terms of cumulative percentages and percentages, as shown in the appendix section. The output also shows the percentage and frequency of the patients discharged in each of the thirty-six states provided in the dataset. 

Statistical Analysis 

The mean number of people discharged in 2018 was 699,602. This indicates that the states and an increment in the number of discharged individuals in comparison to the previous years. In addition, the analysis showed that the mode was 53,560, the median was 481,359, and the standard deviation was 802,921.95. Besides, while the minimum was found to be 53,560, the maximum value was 3,819,392. 

Histogram and Bar Graphs 

The pictorial representation of the analyzed data enhances the understanding of the observed trends. The histogram presents a data frequency with a normal curve which is important in showing the skewness of the data (Shreffler& Huecker, 2022). Skewness is usually used in measuring data asymmetry or how the values are distributed around the mean. The histogram also played a key role in visualizing the number of patients discharged during that time. The data on patient discharge did not show a normal distribution, which is an indication of skewed data. 

Applying Descriptive statistical analysis to the Prospectus 

Descriptive statistics can be useful in coming up with summaries for the overall data before a comprehensive conclusion about the study can be made (Heavey, 2022). Therefore, various strategies can be used, including bar charts, histograms, tables, and other visual displays. The information obtained from descriptive statistics also simplifies data substantially, especially data from a big population which would otherwise prove to be complicated without using descriptive statistics (MacFarland et al., 2021). The implication is that it enhances an understanding of the whole data set. 

Conclusion 

This write-up has explored working with descriptive statistics. Various aspects have been explored in the statistical analyses, including histogram and summary of results such as mean, mode, median, maximum, and minimum. The paper also describes ways of applying descriptive statistical analysis to the Prospectus of this DNP project. Applying descriptive statistics in this project would also heighten the readers’ understanding of the simple analytical techniques used in analyzing DNP project data.  

References 

Grove, S. K., & Cipher, D. J. (2019). Statistics for nursing research-e-book: a workbook for evidence-based practice. Elsevier Health Sciences. 

Heavey, E. (2022). Statistics for nursing: A practical approach. Jones & Bartlett Learning. 

Kaur, P., Stoltzfus, J., & Yellapu, V. (2018). Descriptive statistics. International Journal of Academic Medicine, 4(1), 60. Doi: 10.4103/IJAM.IJAM_7_18 

MacFarland, T. W., Yates, J. M., MacFarland, T. W., & Yates, J. M. (2021). Data Exploration, Descriptive Statistics, and Measures of Central Tendency. Using R for Biostatistics, 57-139. Doi: 10.1007/978-3-030-62404-0_2 

Pallant, J. (2020). SPSS survival manual: A step by step guide to data analysis using IBM SPSS. 

Shreffler, J., & Huecker, M. R. (2022). Exploratory data analysis: Frequencies, descriptive statistics, histograms, and boxplots. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557570/ 

Appendices 

The Histogram